Posts By:
Dan Kuster

In part one, we showed how the machine learning process is like the scientific thinking process, and in part two, we introduced a benchmark task and showed how to get your machine learning system up and running with a simple nearest neighbors model. Now you have all the necessary parts of a machine learning system:… Read more

Here we introduce optical character recognition as a common benchmark task in modern machine learning, and show how to implement a simple model. Being able to experiment with machine learning models is the first step towards capability! Scientific learning process –> machine learning process In the previous post, we introduced machine learning as a principled… Read more

Hello, friendly human! Welcome to the first in a series of articles about using machine learning to solve problems. We start with fundamental concepts, and later explain how to implement those concepts using Python + TensorFlow code. Then we’ll show how to combine and extend these fundamental concepts to solve more interesting problems. An unproductive… Read more

In this case study, we evaluate four different strategies for solving a problem with machine learning. In terms of both technical performance and practical factors like economics and amount of training data required, customized models built from semi-supervised “deep” features using transfer learning outperform models built from scratch, and rival state-of-the-art methods. Featured on KDnuggets.… Read more

We visited the second annual RE•WORK Deep Learning conference in Boston earlier this month. In this debrief, I’m going to share my totally biased take on what was noteworthy from the conference. My observations will be in the context of trends in deep learning tech. As a bonus, since we were one of the few… Read more

A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff) We’ve been using TensorFlow in daily research and engineering since it was released almost six months ago. We’ve learned a lot of things along the way. Time for an update! Because there are many subjective articles on TensorFlow and… Read more

Maybe you’re training a machine learning model on a really big dataset. Perhaps you’ve got a big database dump and you want to extract some information. Or maybe you’re crawling web scrapes or mining text files. Modern computers are really quite powerful for processing streams of data. You shouldn’t have to resort to a Hadoop… Read more

In the frothy sea of Big Data buzz, there’s a tidbit: “More data beats a better model.” But if you’re not Google, and you’re not building distributed language models…well, haven’t you ever wondered how much improvement a model should yield when scaling up to a bigger dataset? Here we look at a specific example to… Read more

Earlier this week, Google released TensorFlow, an open source library for numerical computation. Given the general frothiness around machine learning, we thought folks might appreciate a simple, straightshootin’ take from indico’s Machine Learning team. Unlike a random person on the Internet, we deal with this stuff daily, and can hopefully shed some light on how… Read more

Get the ipython/Jupyter notebook on Github: indico-plotlines A few months ago, a great video of Kurt Vonnegut circulated the web. He describes an idea for plotting the simple shapes of stories as good vs. ill fortune across the length of the story. He says: “There’s no reason why these simple shapes of stories can’t be fed… Read more